import numpy as np import gradio as gr from Segmentation.segmentation import get_mask,replace_sofa from StyleTransfer.styleTransfer import resize_sofa,resize_style,create_styledSofa from PIL import Image def style_sofa(input_img: np.ndarray, style_img: np.ndarray): """ Styles (all) the sofas in the image to the given style. This function uses a transformer to combine the image with the desired style according to a generated mask of the sofas in the image. Input: input_img = image containing a sofa style_img = image containing a style Return: new_sofa = image containing the styled sofa """ # preprocess input images to be (640,640) squares to fit requirements of the segmentation model resized_img = resize_sofa(input_img) resized_style = resize_style(style_img) # generate mask for image mask = get_mask(resized_img) styled_sofa = create_styledSofa(resized_img,resized_style) new_sofa = replace_sofa(resized_img,mask,styled_sofa) return new_sofa image = gr.inputs.Image() style = gr.inputs.Image() demo = gr.Interface( style_sofa, [image,style], 'image', examples=[ ['input/sofa_example1.jpg','input/style_example1.jpg'], ['input/sofa_example1.jpg','input/style_example2.jpg'], ['input/sofa_example1.jpg','input/style_example3.jpg'], ['input/sofa_example1.jpg','input/style_example4.jpg'], ['input/sofa_example1.jpg','input/style_example5.jpg'], ], title="Style your sofa", description="🛋 Customize your sofa to your wildest dreams! 🛋", ) if __name__ == "__main__": demo.launch(share=True) #https://github.com/dhawan98/Post-Processing-of-Image-Segmentation-using-CRF